Roundup It's been an interesting fortnight, sorry, two weeks in AI. In addition to what we've already reported, we have news about HPE developing what looks like a neural network accelerator chip, TensorFlow updates, Google's effort to teach software to make software, and other bits and pieces. The next biz in line claiming to be working on developing a fast custom-designed chip supposedly for neural networks is HPE. It was first reported on our sister site, The Next Platform, this week, although there are scant details on how the hardware works, its specs or even if it can deal with stuff like deep learning. That's mainly because our colleagues got wind of the secret R&D effort before it was due to be made public, and the enterprise IT giant is keeping schtum for now. The mysterious chip's "dot product engine" (DPE) architecture is apparently geared toward carrying out matrix operations at speed, which is useful for executing AI algorithms quickly.

THAT a computer program can repeatedly beat the world champion at Go, a complex board game, is a coup for the fast-moving field of artificial intelligence (AI). Another high-stakes game, however, is taking place behind the scenes, as firms compete to hire the smartest AI experts. Technology giants, including Google, Facebook, Microsoft and Baidu, are racing to expand their AI activities. Last year they spent some $8.5 billion on deals, says Quid, a data firm. That was four times more than in 2010.

Neural networks are ruling the field of artificial intelligence. Let us help you join the conversation. If you've spent any time reading about artificial intelligence, you'll almost certainly have heard about artificial neural networks. But what exactly is one? Rather than enrolling in a comprehensive computer science course or delving into some of the more in-depth resources that are available online, check out our handy layperson's guide to get a quick and easy introduction to this amazing form of machine learning. Artificial neural networks are one of the main tools used in machine learning.

Deep-learning software attempts to mimic human brain activity in the neocortex, where thinking occurs. The software learns to recognize patterns in digital representations of sounds, images, and other data. Ray Kurzweil wrote a definitive book "How to Create a Mind" through software techniques. The goal of deep learning is to recreate human intelligence at a machine level, hence, Artificial Intelligence. However, the outcome of such learning is predicated on how well the software is trained.

We exploit a recently derived inversion scheme for arbitrary deep neural networks to develop a new semi-supervised learning framework that applies to a wide range of systems and problems. The approach outperforms current state-of-the-art methods on MNIST reaching $99.14\%$ of test set accuracy while using $5$ labeled examples per class. Experiments with one-dimensional signals highlight the generality of the method. Importantly, our approach is simple, efficient, and requires no change in the deep network architecture.

Image understanding using deep convolutional network has reached human-level performance, yet a closely related problem of video understanding especially, action recognition has not reached the requisite level of maturity. We combine multi-kernels based support-vector-machines (SVM) with a multi-stream deep convolutional neural network to achieve close to state-of-the-art performance on a 51-class activity recognition problem (HMDB-51 dataset); this specific dataset has proved to be particularly challenging for deep neural networks due to the heterogeneity in camera viewpoints, video quality, etc. The resulting architecture is named pillar networks as each (very) deep neural network acts as a pillar for the hierarchical classifiers. In addition, we illustrate that hand-crafted features such as improved dense trajectories (iDT) and Multi-skip Feature Stacking (MIFS), as additional pillars, can further supplement the performance.

When used as a surrogate objective for maximum likelihood estimation in latent variable models, the evidence lower bound (ELBO) produces state-of-the-art results. Inspired by this, we consider the extension of the ELBO to a family of lower bounds defined by a particle filter's estimator of the marginal likelihood, the filtering variational objectives (FIVOs). FIVOs take the same arguments as the ELBO, but can exploit a model's sequential structure to form tighter bounds. We present results that relate the tightness of FIVO's bound to the variance of the particle filter's estimator by considering the generic case of bounds defined as log-transformed likelihood estimators. Experimentally, we show that training with FIVO results in substantial improvements over training the same model architecture with the ELBO on sequential data.

Learning an encoding of feature vectors in terms of an over-complete dictionary or a information geometric (Fisher vectors) construct is wide-spread in statistical signal processing and computer vision. In content based information retrieval using deep-learning classifiers, such encodings are learnt on the flattened last layer, without adherence to the multi-linear structure of the underlying feature tensor. We illustrate a variety of feature encodings incl. sparse dictionary coding and Fisher vectors along with proposing that a structured tensor factorization scheme enables us to perform retrieval that can be at par, in terms of average precision, with Fisher vector encoded image signatures. In short, we illustrate how structural constraints increase retrieval fidelity.

In March 2016, Google's Alphago artificial intelligence (AI) program stunned the world by beating the human world champion Go player in front of 200 million spectators. This was living proof of the potential in AI technology and the level of maturity reached by neural network and deep learning technologies. Those astounded by the success included quite a few engineers and managers who have been leading the AI revolution in the world in recent years. One of these was Intel VP Naveen Rao, general manager of the company's Artificial Intelligence Products Group, which was founded last year. "When I studied at college in the 1990s, we regarded artificial intelligence as'creative work'," Rao relates.

To produce high-quality food and feed a growing world population with the given amount of arable land in a sustainable manner, we must develop new methods of sustainable farming that increase yield while minimizing chemical inputs such as fertilizers, herbicides, and pesticides. I and my colleagues are working on a robotics-centered approaches to address this grand challenge. My name is Andres Milioto, and I am a research assistant and Ph.D. student in robotics at the Photogrammetry and Robotics Lab (http://www.ipb.uni-bonn.de) Together with Philipp Lottes, Nived Chebrolu, and our supervisor Prof. Dr. Cyrill Stachniss we are developing an adaptable ground and aerial robots for smart farming in the context of the EC-funded project "Flourish" (http://flourish-project.eu/), where we collaborate with several other Universities and industry partners across Europe. The Flourish consortium is committed to develop new robotic methods for sustainable farming that aim at minimizing chemical inputs such as fertilizers, herbicides, and pesticides in order to reduce the side-effects on our environment.